Overview

Brought to you by YData

Dataset statistics

Number of variables18
Number of observations20058
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory17.4 MiB
Average record size in memory907.4 B

Variable types

Numeric6
Boolean1
Categorical3
Text8

Alerts

black_rating is highly overall correlated with white_ratingHigh correlation
victory_status is highly overall correlated with winnerHigh correlation
white_rating is highly overall correlated with black_ratingHigh correlation
winner is highly overall correlated with victory_statusHigh correlation
opening_response is highly imbalanced (79.0%) Imbalance
game_id is uniformly distributed Uniform
game_id has unique values Unique
rating_difference has 203 (1.0%) zeros Zeros

Reproduction

Analysis started2025-01-19 05:07:34.934993
Analysis finished2025-01-19 05:07:56.561447
Duration21.63 seconds
Software versionydata-profiling vv4.12.1
Download configurationconfig.json

Variables

game_id
Real number (ℝ)

Uniform  Unique 

Distinct20058
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10029.5
Minimum1
Maximum20058
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.8 KiB
2025-01-19T05:07:56.753596image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1003.85
Q15015.25
median10029.5
Q315043.75
95-th percentile19055.15
Maximum20058
Range20057
Interquartile range (IQR)10028.5

Descriptive statistics

Standard deviation5790.3902
Coefficient of variation (CV)0.57733588
Kurtosis-1.2
Mean10029.5
Median Absolute Deviation (MAD)5014.5
Skewness0
Sum2.0117171 × 108
Variance33528618
MonotonicityStrictly increasing
2025-01-19T05:07:57.052514image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
13370 1
 
< 0.1%
13377 1
 
< 0.1%
13376 1
 
< 0.1%
13375 1
 
< 0.1%
13374 1
 
< 0.1%
13373 1
 
< 0.1%
13372 1
 
< 0.1%
13371 1
 
< 0.1%
13369 1
 
< 0.1%
Other values (20048) 20048
> 99.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
20058 1
< 0.1%
20057 1
< 0.1%
20056 1
< 0.1%
20055 1
< 0.1%
20054 1
< 0.1%
20053 1
< 0.1%
20052 1
< 0.1%
20051 1
< 0.1%
20050 1
< 0.1%
20049 1
< 0.1%

rated
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size19.8 KiB
True
16155 
False
3903 
ValueCountFrequency (%)
True 16155
80.5%
False 3903
 
19.5%
2025-01-19T05:07:57.309320image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

turns
Real number (ℝ)

Distinct211
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean60.465999
Minimum1
Maximum349
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.8 KiB
2025-01-19T05:07:57.594090image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile14
Q137
median55
Q379
95-th percentile124
Maximum349
Range348
Interquartile range (IQR)42

Descriptive statistics

Standard deviation33.570585
Coefficient of variation (CV)0.55519772
Kurtosis1.3851607
Mean60.465999
Median Absolute Deviation (MAD)20
Skewness0.89728377
Sum1212827
Variance1126.9842
MonotonicityNot monotonic
2025-01-19T05:07:57.890300image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
53 303
 
1.5%
45 302
 
1.5%
51 299
 
1.5%
57 297
 
1.5%
39 297
 
1.5%
41 295
 
1.5%
43 293
 
1.5%
52 290
 
1.4%
54 283
 
1.4%
47 283
 
1.4%
Other values (201) 17116
85.3%
ValueCountFrequency (%)
1 18
 
0.1%
2 185
0.9%
3 87
0.4%
4 52
 
0.3%
5 40
 
0.2%
6 35
 
0.2%
7 70
 
0.3%
8 54
 
0.3%
9 76
0.4%
10 64
 
0.3%
ValueCountFrequency (%)
349 2
< 0.1%
259 1
< 0.1%
255 1
< 0.1%
226 1
< 0.1%
222 2
< 0.1%
221 1
< 0.1%
218 1
< 0.1%
216 1
< 0.1%
212 1
< 0.1%
210 2
< 0.1%

victory_status
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size20.1 KiB
Resign
11147 
Mate
6325 
Out of Time
1680 
Draw
 
906

Length

Max length11
Median length6
Mean length5.6977764
Min length4

Characters and Unicode

Total characters114286
Distinct characters19
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOut of Time
2nd rowResign
3rd rowMate
4th rowMate
5th rowMate

Common Values

ValueCountFrequency (%)
Resign 11147
55.6%
Mate 6325
31.5%
Out of Time 1680
 
8.4%
Draw 906
 
4.5%

Length

2025-01-19T05:07:58.178291image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-19T05:07:58.411958image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
resign 11147
47.6%
mate 6325
27.0%
out 1680
 
7.2%
of 1680
 
7.2%
time 1680
 
7.2%
draw 906
 
3.9%

Most occurring characters

ValueCountFrequency (%)
e 19152
16.8%
i 12827
11.2%
R 11147
9.8%
s 11147
9.8%
g 11147
9.8%
n 11147
9.8%
t 8005
7.0%
a 7231
 
6.3%
M 6325
 
5.5%
3360
 
2.9%
Other values (9) 12798
11.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 89188
78.0%
Uppercase Letter 21738
 
19.0%
Space Separator 3360
 
2.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 19152
21.5%
i 12827
14.4%
s 11147
12.5%
g 11147
12.5%
n 11147
12.5%
t 8005
9.0%
a 7231
 
8.1%
f 1680
 
1.9%
m 1680
 
1.9%
o 1680
 
1.9%
Other values (3) 3492
 
3.9%
Uppercase Letter
ValueCountFrequency (%)
R 11147
51.3%
M 6325
29.1%
T 1680
 
7.7%
O 1680
 
7.7%
D 906
 
4.2%
Space Separator
ValueCountFrequency (%)
3360
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 110926
97.1%
Common 3360
 
2.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 19152
17.3%
i 12827
11.6%
R 11147
10.0%
s 11147
10.0%
g 11147
10.0%
n 11147
10.0%
t 8005
7.2%
a 7231
 
6.5%
M 6325
 
5.7%
f 1680
 
1.5%
Other values (8) 11118
10.0%
Common
ValueCountFrequency (%)
3360
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 114286
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 19152
16.8%
i 12827
11.2%
R 11147
9.8%
s 11147
9.8%
g 11147
9.8%
n 11147
9.8%
t 8005
7.0%
a 7231
 
6.3%
M 6325
 
5.5%
3360
 
2.9%
Other values (9) 12798
11.2%

winner
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size20.0 KiB
White
10001 
Black
9107 
Draw
 
950

Length

Max length5
Median length5
Mean length4.9526374
Min length4

Characters and Unicode

Total characters99340
Distinct characters13
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWhite
2nd rowBlack
3rd rowWhite
4th rowWhite
5th rowWhite

Common Values

ValueCountFrequency (%)
White 10001
49.9%
Black 9107
45.4%
Draw 950
 
4.7%

Length

2025-01-19T05:07:58.645789image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-19T05:07:58.885229image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
white 10001
49.9%
black 9107
45.4%
draw 950
 
4.7%

Most occurring characters

ValueCountFrequency (%)
a 10057
10.1%
W 10001
10.1%
h 10001
10.1%
i 10001
10.1%
t 10001
10.1%
e 10001
10.1%
B 9107
9.2%
l 9107
9.2%
c 9107
9.2%
k 9107
9.2%
Other values (3) 2850
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 79282
79.8%
Uppercase Letter 20058
 
20.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 10057
12.7%
h 10001
12.6%
i 10001
12.6%
t 10001
12.6%
e 10001
12.6%
l 9107
11.5%
c 9107
11.5%
k 9107
11.5%
r 950
 
1.2%
w 950
 
1.2%
Uppercase Letter
ValueCountFrequency (%)
W 10001
49.9%
B 9107
45.4%
D 950
 
4.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 99340
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 10057
10.1%
W 10001
10.1%
h 10001
10.1%
i 10001
10.1%
t 10001
10.1%
e 10001
10.1%
B 9107
9.2%
l 9107
9.2%
c 9107
9.2%
k 9107
9.2%
Other values (3) 2850
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 99340
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 10057
10.1%
W 10001
10.1%
h 10001
10.1%
i 10001
10.1%
t 10001
10.1%
e 10001
10.1%
B 9107
9.2%
l 9107
9.2%
c 9107
9.2%
k 9107
9.2%
Other values (3) 2850
 
2.9%

time_increment
Text

Distinct400
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
2025-01-19T05:07:59.347222image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length7
Median length4
Mean length3.9943663
Min length3

Characters and Unicode

Total characters80119
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique94 ?
Unique (%)0.5%

Sample

1st row15+2
2nd row5+10
3rd row5+10
4th row20+0
5th row30+3
ValueCountFrequency (%)
10+0 7721
38.5%
15+0 1311
 
6.5%
15+15 850
 
4.2%
5+5 738
 
3.7%
5+8 697
 
3.5%
8+0 588
 
2.9%
10+5 579
 
2.9%
15+10 461
 
2.3%
20+0 448
 
2.2%
30+0 375
 
1.9%
Other values (390) 6290
31.4%
2025-01-19T05:08:00.158853image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 24696
30.8%
+ 20058
25.0%
1 16956
21.2%
5 9109
 
11.4%
2 2498
 
3.1%
8 2424
 
3.0%
3 1679
 
2.1%
7 912
 
1.1%
4 705
 
0.9%
6 644
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 60061
75.0%
Math Symbol 20058
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 24696
41.1%
1 16956
28.2%
5 9109
 
15.2%
2 2498
 
4.2%
8 2424
 
4.0%
3 1679
 
2.8%
7 912
 
1.5%
4 705
 
1.2%
6 644
 
1.1%
9 438
 
0.7%
Math Symbol
ValueCountFrequency (%)
+ 20058
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 80119
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 24696
30.8%
+ 20058
25.0%
1 16956
21.2%
5 9109
 
11.4%
2 2498
 
3.1%
8 2424
 
3.0%
3 1679
 
2.1%
7 912
 
1.1%
4 705
 
0.9%
6 644
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 80119
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 24696
30.8%
+ 20058
25.0%
1 16956
21.2%
5 9109
 
11.4%
2 2498
 
3.1%
8 2424
 
3.0%
3 1679
 
2.1%
7 912
 
1.1%
4 705
 
0.9%
6 644
 
0.8%

white_id
Text

Distinct9438
Distinct (%)47.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
2025-01-19T05:08:00.530628image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length20
Median length16
Mean length9.2783927
Min length2

Characters and Unicode

Total characters186106
Distinct characters40
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7226 ?
Unique (%)36.0%

Sample

1st rowbourgris
2nd rowa-00
3rd rowischia
4th rowdaniamurashov
5th rownik221107
ValueCountFrequency (%)
taranga 72
 
0.4%
chess-brahs 53
 
0.3%
a_p_t_e_m_u_u 49
 
0.2%
ssf7 48
 
0.2%
bleda 48
 
0.2%
hassan1365416 44
 
0.2%
khelil 41
 
0.2%
ozguragarr 38
 
0.2%
1240100948 38
 
0.2%
anakgreget 38
 
0.2%
Other values (9428) 19589
97.7%
2025-01-19T05:08:01.184970image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 19269
 
10.4%
e 14410
 
7.7%
r 11834
 
6.4%
i 11551
 
6.2%
s 11157
 
6.0%
o 10889
 
5.9%
n 10173
 
5.5%
t 7937
 
4.3%
l 7620
 
4.1%
h 6417
 
3.4%
Other values (30) 74849
40.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 164062
88.2%
Decimal Number 19068
 
10.2%
Connector Punctuation 2134
 
1.1%
Dash Punctuation 838
 
0.5%
Uppercase Letter 4
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 19269
 
11.7%
e 14410
 
8.8%
r 11834
 
7.2%
i 11551
 
7.0%
s 11157
 
6.8%
o 10889
 
6.6%
n 10173
 
6.2%
t 7937
 
4.8%
l 7620
 
4.6%
h 6417
 
3.9%
Other values (16) 52805
32.2%
Decimal Number
ValueCountFrequency (%)
1 3777
19.8%
0 2772
14.5%
2 2332
12.2%
9 1980
10.4%
3 1543
8.1%
7 1516
8.0%
4 1345
 
7.1%
5 1320
 
6.9%
6 1265
 
6.6%
8 1218
 
6.4%
Uppercase Letter
ValueCountFrequency (%)
M 3
75.0%
J 1
 
25.0%
Connector Punctuation
ValueCountFrequency (%)
_ 2134
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 838
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 164066
88.2%
Common 22040
 
11.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 19269
 
11.7%
e 14410
 
8.8%
r 11834
 
7.2%
i 11551
 
7.0%
s 11157
 
6.8%
o 10889
 
6.6%
n 10173
 
6.2%
t 7937
 
4.8%
l 7620
 
4.6%
h 6417
 
3.9%
Other values (18) 52809
32.2%
Common
ValueCountFrequency (%)
1 3777
17.1%
0 2772
12.6%
2 2332
10.6%
_ 2134
9.7%
9 1980
9.0%
3 1543
7.0%
7 1516
6.9%
4 1345
 
6.1%
5 1320
 
6.0%
6 1265
 
5.7%
Other values (2) 2056
9.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 186106
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 19269
 
10.4%
e 14410
 
7.7%
r 11834
 
6.4%
i 11551
 
6.2%
s 11157
 
6.0%
o 10889
 
5.9%
n 10173
 
5.5%
t 7937
 
4.3%
l 7620
 
4.1%
h 6417
 
3.4%
Other values (30) 74849
40.2%

white_rating
Real number (ℝ)

High correlation 

Distinct1516
Distinct (%)7.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1596.6319
Minimum784
Maximum2700
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.8 KiB
2025-01-19T05:08:01.456664image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum784
5-th percentile1144
Q11398
median1567
Q31793
95-th percentile2111
Maximum2700
Range1916
Interquartile range (IQR)395

Descriptive statistics

Standard deviation291.25338
Coefficient of variation (CV)0.18241736
Kurtosis0.0089036394
Mean1596.6319
Median Absolute Deviation (MAD)195
Skewness0.30076618
Sum32025242
Variance84828.529
MonotonicityNot monotonic
2025-01-19T05:08:01.744756image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1500 812
 
4.0%
1480 51
 
0.3%
1400 48
 
0.2%
1536 46
 
0.2%
1708 45
 
0.2%
1501 44
 
0.2%
1527 43
 
0.2%
1562 43
 
0.2%
1621 42
 
0.2%
1383 42
 
0.2%
Other values (1506) 18842
93.9%
ValueCountFrequency (%)
784 2
< 0.1%
788 1
< 0.1%
793 1
< 0.1%
795 1
< 0.1%
798 2
< 0.1%
801 1
< 0.1%
807 1
< 0.1%
808 1
< 0.1%
810 1
< 0.1%
813 1
< 0.1%
ValueCountFrequency (%)
2700 1
 
< 0.1%
2622 1
 
< 0.1%
2621 24
0.1%
2619 2
 
< 0.1%
2617 1
 
< 0.1%
2613 1
 
< 0.1%
2586 1
 
< 0.1%
2579 1
 
< 0.1%
2574 1
 
< 0.1%
2549 2
 
< 0.1%

black_id
Text

Distinct9331
Distinct (%)46.5%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
2025-01-19T05:08:02.169713image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length20
Median length16
Mean length9.3239107
Min length2

Characters and Unicode

Total characters187019
Distinct characters39
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7148 ?
Unique (%)35.6%

Sample

1st rowa-00
2nd rowskinnerua
3rd rowa-00
4th rowadivanov2009
5th rowadivanov2009
ValueCountFrequency (%)
taranga 82
 
0.4%
vladimir-kramnik-1 60
 
0.3%
a_p_t_e_m_u_u 47
 
0.2%
docboss 44
 
0.2%
king5891 44
 
0.2%
ducksandcats 41
 
0.2%
cape217 38
 
0.2%
saviter 38
 
0.2%
anakgreget 36
 
0.2%
artem555 34
 
0.2%
Other values (9321) 19594
97.7%
2025-01-19T05:08:02.857650image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 19463
 
10.4%
e 14290
 
7.6%
r 12002
 
6.4%
i 11740
 
6.3%
o 11181
 
6.0%
s 11118
 
5.9%
n 10488
 
5.6%
t 7993
 
4.3%
l 7551
 
4.0%
m 6507
 
3.5%
Other values (29) 74686
39.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 165989
88.8%
Decimal Number 18100
 
9.7%
Connector Punctuation 2114
 
1.1%
Dash Punctuation 815
 
0.4%
Uppercase Letter 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 19463
 
11.7%
e 14290
 
8.6%
r 12002
 
7.2%
i 11740
 
7.1%
o 11181
 
6.7%
s 11118
 
6.7%
n 10488
 
6.3%
t 7993
 
4.8%
l 7551
 
4.5%
m 6507
 
3.9%
Other values (16) 53656
32.3%
Decimal Number
ValueCountFrequency (%)
1 3525
19.5%
0 2533
14.0%
2 2351
13.0%
9 1978
10.9%
7 1575
8.7%
3 1417
7.8%
4 1229
 
6.8%
5 1213
 
6.7%
8 1142
 
6.3%
6 1137
 
6.3%
Connector Punctuation
ValueCountFrequency (%)
_ 2114
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 815
100.0%
Uppercase Letter
ValueCountFrequency (%)
J 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 165990
88.8%
Common 21029
 
11.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 19463
 
11.7%
e 14290
 
8.6%
r 12002
 
7.2%
i 11740
 
7.1%
o 11181
 
6.7%
s 11118
 
6.7%
n 10488
 
6.3%
t 7993
 
4.8%
l 7551
 
4.5%
m 6507
 
3.9%
Other values (17) 53657
32.3%
Common
ValueCountFrequency (%)
1 3525
16.8%
0 2533
12.0%
2 2351
11.2%
_ 2114
10.1%
9 1978
9.4%
7 1575
7.5%
3 1417
6.7%
4 1229
 
5.8%
5 1213
 
5.8%
8 1142
 
5.4%
Other values (2) 1952
9.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 187019
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 19463
 
10.4%
e 14290
 
7.6%
r 12002
 
6.4%
i 11740
 
6.3%
o 11181
 
6.0%
s 11118
 
5.9%
n 10488
 
5.6%
t 7993
 
4.3%
l 7551
 
4.0%
m 6507
 
3.5%
Other values (29) 74686
39.9%

black_rating
Real number (ℝ)

High correlation 

Distinct1521
Distinct (%)7.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1588.832
Minimum789
Maximum2723
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.8 KiB
2025-01-19T05:08:03.440215image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum789
5-th percentile1135
Q11391
median1562
Q31784
95-th percentile2105.15
Maximum2723
Range1934
Interquartile range (IQR)393

Descriptive statistics

Standard deviation291.03613
Coefficient of variation (CV)0.18317615
Kurtosis-0.072277075
Mean1588.832
Median Absolute Deviation (MAD)193
Skewness0.25851033
Sum31868792
Variance84702.027
MonotonicityNot monotonic
2025-01-19T05:08:03.729443image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1500 797
 
4.0%
1400 69
 
0.3%
1501 53
 
0.3%
1810 49
 
0.2%
1562 45
 
0.2%
1466 42
 
0.2%
1621 41
 
0.2%
1802 41
 
0.2%
1484 41
 
0.2%
1480 40
 
0.2%
Other values (1511) 18840
93.9%
ValueCountFrequency (%)
789 1
 
< 0.1%
791 1
 
< 0.1%
795 2
< 0.1%
796 1
 
< 0.1%
800 1
 
< 0.1%
804 1
 
< 0.1%
806 1
 
< 0.1%
807 4
< 0.1%
818 1
 
< 0.1%
820 1
 
< 0.1%
ValueCountFrequency (%)
2723 1
 
< 0.1%
2621 15
0.1%
2588 1
 
< 0.1%
2577 1
 
< 0.1%
2571 1
 
< 0.1%
2540 1
 
< 0.1%
2526 1
 
< 0.1%
2524 1
 
< 0.1%
2516 1
 
< 0.1%
2501 1
 
< 0.1%

moves
Text

Distinct18920
Distinct (%)94.3%
Missing0
Missing (%)0.0%
Memory size5.9 MiB
2025-01-19T05:08:04.299060image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length1413
Median length604
Mean length249.65171
Min length2

Characters and Unicode

Total characters5007514
Distinct characters28
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique18074 ?
Unique (%)90.1%

Sample

1st rowd4 d5 c4 c6 cxd5 e6 dxe6 fxe6 Nf3 Bb4+ Nc3 Ba5 Bf4
2nd rowd4 Nc6 e4 e5 f4 f6 dxe5 fxe5 fxe5 Nxe5 Qd4 Nc6 Qe5+ Nxe5 c4 Bb4+
3rd rowe4 e5 d3 d6 Be3 c6 Be2 b5 Nd2 a5 a4 c5 axb5 Nc6 bxc6 Ra6 Nc4 a4 c3 a3 Nxa3 Rxa3 Rxa3 c4 dxc4 d5 cxd5 Qxd5 exd5 Be6 Ra8+ Ke7 Bc5+ Kf6 Bxf8 Kg6 Bxg7 Kxg7 dxe6 Kh6 exf7 Nf6 Rxh8 Nh5 Bxh5 Kg5 Rxh7 Kf5 Qf3+ Ke6 Bg4+ Kd6 Rh6+ Kc5 Qe3+ Kb5 c4+ Kb4 Qc3+ Ka4 Bd1#
4th rowd4 d5 Nf3 Bf5 Nc3 Nf6 Bf4 Ng4 e3 Nc6 Be2 Qd7 O-O O-O-O Nb5 Nb4 Rc1 Nxa2 Ra1 Nb4 Nxa7+ Kb8 Nb5 Bxc2 Bxc7+ Kc8 Qd2 Qc6 Na7+ Kd7 Nxc6 bxc6 Bxd8 Kxd8 Qxb4 e5 Qb8+ Ke7 dxe5 Be4 Ra7+ Ke6 Qe8+ Kf5 Qxf7+ Nf6 Nh4+ Kg5 g3 Ng4 Qf4+ Kh5 Qxg4+ Kh6 Qf4+ g5 Qf6+ Bg6 Nxg6 Bg7 Qxg7#
5th rowe4 e5 Nf3 d6 d4 Nc6 d5 Nb4 a3 Na6 Nc3 Be7 b4 Nf6 Bg5 O-O b5 Nc5 Bxf6 Bxf6 Bd3 Qd7 O-O Nxd3 Qxd3 c6 a4 cxd5 Nxd5 Qe6 Nc7 Qg4 Nxa8 Bd7 Nc7 Rc8 Nd5 Qg6 Nxf6+ Qxf6 Rfd1 Re8 Qxd6 Bg4 Qxf6 gxf6 Rd3 Bxf3 Rxf3 Rd8 Rxf6 Kg7 Rf3 Rd2 Rg3+ Kf8 c3 Re2 f3 Rc2 Rg5 f6 Rh5 Kg7 Rd1 Kg6 Rh3 Rxc3 Rd7 Rc1+ Kf2 Rc2+ Kg3 h5 Rxb7 Kg5 Rxa7 h4+ Rxh4 Rxg2+ Kxg2 Kxh4 b6 Kg5 b7 f5 exf5 Kxf5 b8=Q e4 Rf7+ Kg5 Qg8+ Kh6 Rh7#
ValueCountFrequency (%)
o-o 23433
 
1.9%
nf3 19832
 
1.6%
e4 18404
 
1.5%
nf6 17835
 
1.5%
d4 17063
 
1.4%
e5 16912
 
1.4%
nc6 15338
 
1.3%
d5 15141
 
1.2%
nc3 14886
 
1.2%
c5 10590
 
0.9%
Other values (2733) 1043393
86.0%
2025-01-19T05:08:05.197066image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1192769
23.8%
x 287906
 
5.7%
d 237741
 
4.7%
e 234342
 
4.7%
5 225427
 
4.5%
4 222988
 
4.5%
N 213949
 
4.3%
6 200356
 
4.0%
f 193986
 
3.9%
c 191180
 
3.8%
Other values (18) 1806870
36.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1580667
31.6%
Space Separator 1192769
23.8%
Decimal Number 1187319
23.7%
Uppercase Letter 910860
18.2%
Math Symbol 98545
 
2.0%
Dash Punctuation 31029
 
0.6%
Other Punctuation 6325
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
x 287906
18.2%
d 237741
15.0%
e 234342
14.8%
f 193986
12.3%
c 191180
12.1%
g 130857
8.3%
b 124180
7.9%
h 92142
 
5.8%
a 88333
 
5.6%
Decimal Number
ValueCountFrequency (%)
5 225427
19.0%
4 222988
18.8%
6 200356
16.9%
3 189213
15.9%
7 107569
9.1%
2 96578
8.1%
8 73439
 
6.2%
1 71749
 
6.0%
Uppercase Letter
ValueCountFrequency (%)
N 213949
23.5%
B 187739
20.6%
R 165167
18.1%
Q 154368
16.9%
K 131377
14.4%
O 58260
 
6.4%
Math Symbol
ValueCountFrequency (%)
+ 95351
96.8%
= 3194
 
3.2%
Space Separator
ValueCountFrequency (%)
1192769
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 31029
100.0%
Other Punctuation
ValueCountFrequency (%)
# 6325
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2515987
50.2%
Latin 2491527
49.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
x 287906
11.6%
d 237741
9.5%
e 234342
9.4%
N 213949
8.6%
f 193986
 
7.8%
c 191180
 
7.7%
B 187739
 
7.5%
R 165167
 
6.6%
Q 154368
 
6.2%
K 131377
 
5.3%
Other values (5) 493772
19.8%
Common
ValueCountFrequency (%)
1192769
47.4%
5 225427
 
9.0%
4 222988
 
8.9%
6 200356
 
8.0%
3 189213
 
7.5%
7 107569
 
4.3%
2 96578
 
3.8%
+ 95351
 
3.8%
8 73439
 
2.9%
1 71749
 
2.9%
Other values (3) 40548
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5007514
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1192769
23.8%
x 287906
 
5.7%
d 237741
 
4.7%
e 234342
 
4.7%
5 225427
 
4.5%
4 222988
 
4.5%
N 213949
 
4.3%
6 200356
 
4.0%
f 193986
 
3.9%
c 191180
 
3.8%
Other values (18) 1806870
36.1%

opening_code
Text

Distinct365
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
2025-01-19T05:08:05.936326image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters60174
Distinct characters15
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique51 ?
Unique (%)0.3%

Sample

1st rowD10
2nd rowB00
3rd rowC20
4th rowD02
5th rowC41
ValueCountFrequency (%)
a00 1007
 
5.0%
c00 844
 
4.2%
d00 739
 
3.7%
b01 716
 
3.6%
c41 691
 
3.4%
c20 675
 
3.4%
a40 618
 
3.1%
b00 611
 
3.0%
b20 567
 
2.8%
c50 538
 
2.7%
Other values (355) 13052
65.1%
2025-01-19T05:08:06.900533image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 15274
25.4%
C 7658
12.7%
4 5415
 
9.0%
2 5360
 
8.9%
B 5238
 
8.7%
1 4199
 
7.0%
A 3973
 
6.6%
5 3336
 
5.5%
D 2683
 
4.5%
3 2139
 
3.6%
Other values (5) 4899
 
8.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 40116
66.7%
Uppercase Letter 20058
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15274
38.1%
4 5415
 
13.5%
2 5360
 
13.4%
1 4199
 
10.5%
5 3336
 
8.3%
3 2139
 
5.3%
6 1851
 
4.6%
7 1265
 
3.2%
8 849
 
2.1%
9 428
 
1.1%
Uppercase Letter
ValueCountFrequency (%)
C 7658
38.2%
B 5238
26.1%
A 3973
19.8%
D 2683
 
13.4%
E 506
 
2.5%

Most occurring scripts

ValueCountFrequency (%)
Common 40116
66.7%
Latin 20058
33.3%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15274
38.1%
4 5415
 
13.5%
2 5360
 
13.4%
1 4199
 
10.5%
5 3336
 
8.3%
3 2139
 
5.3%
6 1851
 
4.6%
7 1265
 
3.2%
8 849
 
2.1%
9 428
 
1.1%
Latin
ValueCountFrequency (%)
C 7658
38.2%
B 5238
26.1%
A 3973
19.8%
D 2683
 
13.4%
E 506
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 60174
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15274
25.4%
C 7658
12.7%
4 5415
 
9.0%
2 5360
 
8.9%
B 5238
 
8.7%
1 4199
 
7.0%
A 3973
 
6.6%
5 3336
 
5.5%
D 2683
 
4.5%
3 2139
 
3.6%
Other values (5) 4899
 
8.1%

opening_moves
Real number (ℝ)

Distinct23
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.8169808
Minimum1
Maximum28
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.8 KiB
2025-01-19T05:08:07.278692image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median4
Q36
95-th percentile10
Maximum28
Range27
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.7971518
Coefficient of variation (CV)0.58068569
Kurtosis3.0896939
Mean4.8169808
Median Absolute Deviation (MAD)2
Skewness1.3345569
Sum96619
Variance7.8240583
MonotonicityNot monotonic
2025-01-19T05:08:07.642318image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
3 3490
17.4%
4 3308
16.5%
2 2935
14.6%
5 2730
13.6%
6 2020
10.1%
7 1344
 
6.7%
8 1116
 
5.6%
1 1097
 
5.5%
9 687
 
3.4%
10 432
 
2.2%
Other values (13) 899
 
4.5%
ValueCountFrequency (%)
1 1097
 
5.5%
2 2935
14.6%
3 3490
17.4%
4 3308
16.5%
5 2730
13.6%
6 2020
10.1%
7 1344
 
6.7%
8 1116
 
5.6%
9 687
 
3.4%
10 432
 
2.2%
ValueCountFrequency (%)
28 4
 
< 0.1%
24 1
 
< 0.1%
22 1
 
< 0.1%
20 8
 
< 0.1%
19 11
 
0.1%
18 12
 
0.1%
17 37
0.2%
16 31
0.2%
15 43
0.2%
14 57
0.3%

opening_fullname
Text

Distinct1477
Distinct (%)7.4%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
2025-01-19T05:08:08.248565image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length91
Median length74
Mean length32.396151
Min length9

Characters and Unicode

Total characters649802
Distinct characters68
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique382 ?
Unique (%)1.9%

Sample

1st rowSlav Defense: Exchange Variation
2nd rowNimzowitsch Defense: Kennedy Variation
3rd rowKing's Pawn Game: Leonardis Variation
4th rowQueen's Pawn Game: Zukertort Variation
5th rowPhilidor Defense
ValueCountFrequency (%)
defense 11701
 
13.5%
variation 8024
 
9.3%
game 4931
 
5.7%
3626
 
4.2%
opening 3198
 
3.7%
sicilian 2915
 
3.4%
gambit 2641
 
3.1%
queen's 2544
 
2.9%
pawn 2360
 
2.7%
attack 2340
 
2.7%
Other values (790) 42156
48.8%
2025-01-19T05:08:09.282278image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
70004
 
10.8%
e 69917
 
10.8%
n 62467
 
9.6%
i 56030
 
8.6%
a 52492
 
8.1%
s 28432
 
4.4%
t 27785
 
4.3%
o 24021
 
3.7%
r 23607
 
3.6%
c 16165
 
2.5%
Other values (58) 218882
33.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 466601
71.8%
Uppercase Letter 83684
 
12.9%
Space Separator 70004
 
10.8%
Other Punctuation 20862
 
3.2%
Math Symbol 3626
 
0.6%
Dash Punctuation 2713
 
0.4%
Decimal Number 2196
 
0.3%
Open Punctuation 58
 
< 0.1%
Close Punctuation 58
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 69917
15.0%
n 62467
13.4%
i 56030
12.0%
a 52492
11.2%
s 28432
 
6.1%
t 27785
 
6.0%
o 24021
 
5.1%
r 23607
 
5.1%
c 16165
 
3.5%
l 14831
 
3.2%
Other values (16) 90854
19.5%
Uppercase Letter
ValueCountFrequency (%)
D 13229
15.8%
V 8783
10.5%
G 8446
 
10.1%
S 6966
 
8.3%
K 5684
 
6.8%
A 4638
 
5.5%
P 4599
 
5.5%
O 3969
 
4.7%
C 2990
 
3.6%
Q 2831
 
3.4%
Other values (15) 21549
25.8%
Decimal Number
ValueCountFrequency (%)
2 1340
61.0%
3 470
 
21.4%
4 243
 
11.1%
5 85
 
3.9%
7 29
 
1.3%
6 18
 
0.8%
8 9
 
0.4%
1 2
 
0.1%
Other Punctuation
ValueCountFrequency (%)
: 13134
63.0%
' 5585
26.8%
# 1993
 
9.6%
. 150
 
0.7%
Space Separator
ValueCountFrequency (%)
70004
100.0%
Math Symbol
ValueCountFrequency (%)
| 3626
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2713
100.0%
Open Punctuation
ValueCountFrequency (%)
( 58
100.0%
Close Punctuation
ValueCountFrequency (%)
) 58
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 550285
84.7%
Common 99517
 
15.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 69917
12.7%
n 62467
 
11.4%
i 56030
 
10.2%
a 52492
 
9.5%
s 28432
 
5.2%
t 27785
 
5.0%
o 24021
 
4.4%
r 23607
 
4.3%
c 16165
 
2.9%
l 14831
 
2.7%
Other values (41) 174538
31.7%
Common
ValueCountFrequency (%)
70004
70.3%
: 13134
 
13.2%
' 5585
 
5.6%
| 3626
 
3.6%
- 2713
 
2.7%
# 1993
 
2.0%
2 1340
 
1.3%
3 470
 
0.5%
4 243
 
0.2%
. 150
 
0.2%
Other values (7) 259
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 649802
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
70004
 
10.8%
e 69917
 
10.8%
n 62467
 
9.6%
i 56030
 
8.6%
a 52492
 
8.1%
s 28432
 
4.4%
t 27785
 
4.3%
o 24021
 
3.7%
r 23607
 
3.6%
c 16165
 
2.5%
Other values (58) 218882
33.7%

opening_shortname
Text

Distinct128
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
2025-01-19T05:08:09.792863image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length26
Median length24
Mean length15.163177
Min length6

Characters and Unicode

Total characters304143
Distinct characters54
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)< 0.1%

Sample

1st rowSlav Defense
2nd rowNimzowitsch Defense
3rd rowKing's Pawn Game
4th rowQueen's Pawn Game
5th rowPhilidor Defense
ValueCountFrequency (%)
defense 9037
20.6%
game 4846
 
11.0%
opening 3046
 
6.9%
sicilian 2632
 
6.0%
queen's 2342
 
5.3%
pawn 2324
 
5.3%
king's 1656
 
3.8%
gambit 1529
 
3.5%
french 1412
 
3.2%
italian 981
 
2.2%
Other values (115) 14126
32.2%
2025-01-19T05:08:10.952325image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 45766
15.0%
n 36609
 
12.0%
i 24354
 
8.0%
23873
 
7.8%
a 20818
 
6.8%
s 17307
 
5.7%
D 9306
 
3.1%
f 9131
 
3.0%
m 7411
 
2.4%
c 7021
 
2.3%
Other values (44) 102547
33.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 229068
75.3%
Uppercase Letter 45103
 
14.8%
Space Separator 23873
 
7.8%
Other Punctuation 4843
 
1.6%
Dash Punctuation 1172
 
0.4%
Open Punctuation 42
 
< 0.1%
Close Punctuation 42
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 45766
20.0%
n 36609
16.0%
i 24354
10.6%
a 20818
9.1%
s 17307
 
7.6%
f 9131
 
4.0%
m 7411
 
3.2%
c 7021
 
3.1%
t 6951
 
3.0%
g 6617
 
2.9%
Other values (15) 47083
20.6%
Uppercase Letter
ValueCountFrequency (%)
D 9306
20.6%
G 6751
15.0%
S 4456
9.9%
P 3701
 
8.2%
K 3326
 
7.4%
O 3297
 
7.3%
Q 2342
 
5.2%
I 1881
 
4.2%
F 1795
 
4.0%
R 1348
 
3.0%
Other values (13) 6900
15.3%
Other Punctuation
ValueCountFrequency (%)
' 4795
99.0%
. 48
 
1.0%
Space Separator
ValueCountFrequency (%)
23873
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1172
100.0%
Open Punctuation
ValueCountFrequency (%)
( 42
100.0%
Close Punctuation
ValueCountFrequency (%)
) 42
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 274171
90.1%
Common 29972
 
9.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 45766
16.7%
n 36609
13.4%
i 24354
 
8.9%
a 20818
 
7.6%
s 17307
 
6.3%
D 9306
 
3.4%
f 9131
 
3.3%
m 7411
 
2.7%
c 7021
 
2.6%
t 6951
 
2.5%
Other values (38) 89497
32.6%
Common
ValueCountFrequency (%)
23873
79.7%
' 4795
 
16.0%
- 1172
 
3.9%
. 48
 
0.2%
( 42
 
0.1%
) 42
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 304143
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 45766
15.0%
n 36609
 
12.0%
i 24354
 
8.0%
23873
 
7.8%
a 20818
 
6.8%
s 17307
 
5.7%
D 9306
 
3.1%
f 9131
 
3.0%
m 7411
 
2.4%
c 7021
 
2.3%
Other values (44) 102547
33.7%

opening_response
Categorical

Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
Unknown
18851 
Declined
 
503
Accepted
 
453
Refused
 
251

Length

Max length8
Median length7
Mean length7.0476618
Min length7

Characters and Unicode

Total characters141362
Distinct characters18
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUnknown
2nd rowUnknown
3rd rowUnknown
4th rowUnknown
5th rowUnknown

Common Values

ValueCountFrequency (%)
Unknown 18851
94.0%
Declined 503
 
2.5%
Accepted 453
 
2.3%
Refused 251
 
1.3%

Length

2025-01-19T05:08:11.451933image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-19T05:08:11.879230image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
unknown 18851
94.0%
declined 503
 
2.5%
accepted 453
 
2.3%
refused 251
 
1.3%

Most occurring characters

ValueCountFrequency (%)
n 57056
40.4%
U 18851
 
13.3%
k 18851
 
13.3%
o 18851
 
13.3%
w 18851
 
13.3%
e 2414
 
1.7%
c 1409
 
1.0%
d 1207
 
0.9%
i 503
 
0.4%
l 503
 
0.4%
Other values (8) 2866
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 121304
85.8%
Uppercase Letter 20058
 
14.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 57056
47.0%
k 18851
 
15.5%
o 18851
 
15.5%
w 18851
 
15.5%
e 2414
 
2.0%
c 1409
 
1.2%
d 1207
 
1.0%
i 503
 
0.4%
l 503
 
0.4%
p 453
 
0.4%
Other values (4) 1206
 
1.0%
Uppercase Letter
ValueCountFrequency (%)
U 18851
94.0%
D 503
 
2.5%
A 453
 
2.3%
R 251
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 141362
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 57056
40.4%
U 18851
 
13.3%
k 18851
 
13.3%
o 18851
 
13.3%
w 18851
 
13.3%
e 2414
 
1.7%
c 1409
 
1.0%
d 1207
 
0.9%
i 503
 
0.4%
l 503
 
0.4%
Other values (8) 2866
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 141362
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 57056
40.4%
U 18851
 
13.3%
k 18851
 
13.3%
o 18851
 
13.3%
w 18851
 
13.3%
e 2414
 
1.7%
c 1409
 
1.0%
d 1207
 
0.9%
i 503
 
0.4%
l 503
 
0.4%
Other values (8) 2866
 
2.0%

opening_variation
Text

Distinct616
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
2025-01-19T05:08:12.365128image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length47
Median length29
Mean length13.526324
Min length2

Characters and Unicode

Total characters271311
Distinct characters65
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique126 ?
Unique (%)0.6%

Sample

1st rowExchange Variation
2nd rowKennedy Variation
3rd rowLeonardis Variation
4th rowZukertort Variation
5th rowUnknown
ValueCountFrequency (%)
variation 6505
18.3%
unknown 5660
 
15.9%
defense 2226
 
6.3%
attack 1596
 
4.5%
2 1275
 
3.6%
gambit 892
 
2.5%
knights 537
 
1.5%
exchange 498
 
1.4%
classical 489
 
1.4%
normal 412
 
1.2%
Other values (474) 15402
43.4%
2025-01-19T05:08:13.512988image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 37213
13.7%
a 25671
 
9.5%
i 24480
 
9.0%
o 19976
 
7.4%
e 16736
 
6.2%
t 16121
 
5.9%
15434
 
5.7%
r 13377
 
4.9%
s 8447
 
3.1%
k 7935
 
2.9%
Other values (55) 85921
31.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 214844
79.2%
Uppercase Letter 35105
 
12.9%
Space Separator 15434
 
5.7%
Other Punctuation 2528
 
0.9%
Decimal Number 2008
 
0.7%
Dash Punctuation 1364
 
0.5%
Open Punctuation 14
 
< 0.1%
Close Punctuation 14
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 37213
17.3%
a 25671
11.9%
i 24480
11.4%
o 19976
9.3%
e 16736
7.8%
t 16121
7.5%
r 13377
 
6.2%
s 8447
 
3.9%
k 7935
 
3.7%
w 6906
 
3.2%
Other values (16) 37982
17.7%
Uppercase Letter
ValueCountFrequency (%)
V 6664
19.0%
U 5733
16.3%
A 2951
8.4%
D 2833
 
8.1%
S 1958
 
5.6%
M 1886
 
5.4%
K 1859
 
5.3%
C 1654
 
4.7%
G 1344
 
3.8%
B 1043
 
3.0%
Other values (15) 7180
20.5%
Decimal Number
ValueCountFrequency (%)
2 1288
64.1%
3 429
 
21.4%
4 211
 
10.5%
5 63
 
3.1%
7 15
 
0.7%
8 2
 
0.1%
Other Punctuation
ValueCountFrequency (%)
# 1921
76.0%
' 569
 
22.5%
. 34
 
1.3%
: 4
 
0.2%
Space Separator
ValueCountFrequency (%)
15434
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1364
100.0%
Open Punctuation
ValueCountFrequency (%)
( 14
100.0%
Close Punctuation
ValueCountFrequency (%)
) 14
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 249949
92.1%
Common 21362
 
7.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 37213
14.9%
a 25671
 
10.3%
i 24480
 
9.8%
o 19976
 
8.0%
e 16736
 
6.7%
t 16121
 
6.4%
r 13377
 
5.4%
s 8447
 
3.4%
k 7935
 
3.2%
w 6906
 
2.8%
Other values (41) 73087
29.2%
Common
ValueCountFrequency (%)
15434
72.2%
# 1921
 
9.0%
- 1364
 
6.4%
2 1288
 
6.0%
' 569
 
2.7%
3 429
 
2.0%
4 211
 
1.0%
5 63
 
0.3%
. 34
 
0.2%
7 15
 
0.1%
Other values (4) 34
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 271311
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 37213
13.7%
a 25671
 
9.5%
i 24480
 
9.0%
o 19976
 
7.4%
e 16736
 
6.2%
t 16121
 
5.9%
15434
 
5.7%
r 13377
 
4.9%
s 8447
 
3.1%
k 7935
 
2.9%
Other values (55) 85921
31.7%

rating_difference
Real number (ℝ)

Zeros 

Distinct1577
Distinct (%)7.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.7998803
Minimum-1605
Maximum1499
Zeros203
Zeros (%)1.0%
Negative9671
Negative (%)48.2%
Memory size156.8 KiB
2025-01-19T05:08:14.012546image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum-1605
5-th percentile-398
Q1-108
median3
Q3122
95-th percentile424
Maximum1499
Range3104
Interquartile range (IQR)230

Descriptive statistics

Standard deviation249.03667
Coefficient of variation (CV)31.928268
Kurtosis2.9657735
Mean7.7998803
Median Absolute Deviation (MAD)115
Skewness0.082723375
Sum156450
Variance62019.261
MonotonicityNot monotonic
2025-01-19T05:08:14.376671image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 203
 
1.0%
-16 76
 
0.4%
6 74
 
0.4%
-9 74
 
0.4%
-15 73
 
0.4%
-11 72
 
0.4%
17 71
 
0.4%
5 71
 
0.4%
-2 70
 
0.3%
1 70
 
0.3%
Other values (1567) 19204
95.7%
ValueCountFrequency (%)
-1605 2
< 0.1%
-1407 1
 
< 0.1%
-1290 1
 
< 0.1%
-1273 2
< 0.1%
-1237 1
 
< 0.1%
-1223 1
 
< 0.1%
-1188 1
 
< 0.1%
-1155 1
 
< 0.1%
-1141 1
 
< 0.1%
-1121 3
< 0.1%
ValueCountFrequency (%)
1499 1
< 0.1%
1492 1
< 0.1%
1471 1
< 0.1%
1465 1
< 0.1%
1458 2
< 0.1%
1297 1
< 0.1%
1290 1
< 0.1%
1262 2
< 0.1%
1237 1
< 0.1%
1214 1
< 0.1%

Interactions

2025-01-19T05:07:53.598382image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:07:44.450615image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:07:46.757608image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:07:48.144868image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:07:49.588849image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:07:51.412378image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:07:53.925559image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:07:44.824679image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:07:46.995248image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:07:48.397890image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:07:49.849958image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:07:52.048981image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:07:54.290269image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:07:45.218894image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:07:47.236513image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:07:48.641262image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:07:50.181571image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:07:52.398365image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:07:54.619451image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:07:45.650960image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:07:47.486324image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:07:48.872849image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:07:50.539774image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:07:52.718983image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:07:54.985094image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:07:46.191824image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:07:47.715952image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:07:49.111563image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:07:50.796868image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:07:52.970799image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:07:55.288157image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:07:46.538139image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:07:47.919056image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:07:49.336701image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:07:51.078963image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-01-19T05:07:53.298119image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Correlations

2025-01-19T05:08:14.647855image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
black_ratinggame_idopening_movesopening_responseratedrating_differenceturnsvictory_statuswhite_ratingwinner
black_rating1.0000.1300.2210.0320.110-0.3730.1670.0930.6540.122
game_id0.1301.0000.0470.0310.0720.0010.0500.0380.1280.027
opening_moves0.2210.0471.0000.1160.0210.0270.0510.0480.2500.027
opening_response0.0320.0310.1161.0000.0150.0220.0380.0140.0430.026
rated0.1100.0720.0210.0151.0000.2020.0970.0290.1460.026
rating_difference-0.3730.0010.0270.0220.2021.000-0.0300.0320.3730.245
turns0.1670.0500.0510.0380.097-0.0301.0000.1620.1400.153
victory_status0.0930.0380.0480.0140.0290.0320.1621.0000.0970.690
white_rating0.6540.1280.2500.0430.1460.3730.1400.0971.0000.113
winner0.1220.0270.0270.0260.0260.2450.1530.6900.1131.000

Missing values

2025-01-19T05:07:55.614606image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
A simple visualization of nullity by column.
2025-01-19T05:07:56.185756image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

game_idratedturnsvictory_statuswinnertime_incrementwhite_idwhite_ratingblack_idblack_ratingmovesopening_codeopening_movesopening_fullnameopening_shortnameopening_responseopening_variationrating_difference
01False13Out of TimeWhite15+2bourgris1500a-001191d4 d5 c4 c6 cxd5 e6 dxe6 fxe6 Nf3 Bb4+ Nc3 Ba5 Bf4D105Slav Defense: Exchange VariationSlav DefenseUnknownExchange Variation309
12True16ResignBlack5+10a-001322skinnerua1261d4 Nc6 e4 e5 f4 f6 dxe5 fxe5 fxe5 Nxe5 Qd4 Nc6 Qe5+ Nxe5 c4 Bb4+B004Nimzowitsch Defense: Kennedy VariationNimzowitsch DefenseUnknownKennedy Variation61
23True61MateWhite5+10ischia1496a-001500e4 e5 d3 d6 Be3 c6 Be2 b5 Nd2 a5 a4 c5 axb5 Nc6 bxc6 Ra6 Nc4 a4 c3 a3 Nxa3 Rxa3 Rxa3 c4 dxc4 d5 cxd5 Qxd5 exd5 Be6 Ra8+ Ke7 Bc5+ Kf6 Bxf8 Kg6 Bxg7 Kxg7 dxe6 Kh6 exf7 Nf6 Rxh8 Nh5 Bxh5 Kg5 Rxh7 Kf5 Qf3+ Ke6 Bg4+ Kd6 Rh6+ Kc5 Qe3+ Kb5 c4+ Kb4 Qc3+ Ka4 Bd1#C203King's Pawn Game: Leonardis VariationKing's Pawn GameUnknownLeonardis Variation-4
34True61MateWhite20+0daniamurashov1439adivanov20091454d4 d5 Nf3 Bf5 Nc3 Nf6 Bf4 Ng4 e3 Nc6 Be2 Qd7 O-O O-O-O Nb5 Nb4 Rc1 Nxa2 Ra1 Nb4 Nxa7+ Kb8 Nb5 Bxc2 Bxc7+ Kc8 Qd2 Qc6 Na7+ Kd7 Nxc6 bxc6 Bxd8 Kxd8 Qxb4 e5 Qb8+ Ke7 dxe5 Be4 Ra7+ Ke6 Qe8+ Kf5 Qxf7+ Nf6 Nh4+ Kg5 g3 Ng4 Qf4+ Kh5 Qxg4+ Kh6 Qf4+ g5 Qf6+ Bg6 Nxg6 Bg7 Qxg7#D023Queen's Pawn Game: Zukertort VariationQueen's Pawn GameUnknownZukertort Variation-15
45True95MateWhite30+3nik2211071523adivanov20091469e4 e5 Nf3 d6 d4 Nc6 d5 Nb4 a3 Na6 Nc3 Be7 b4 Nf6 Bg5 O-O b5 Nc5 Bxf6 Bxf6 Bd3 Qd7 O-O Nxd3 Qxd3 c6 a4 cxd5 Nxd5 Qe6 Nc7 Qg4 Nxa8 Bd7 Nc7 Rc8 Nd5 Qg6 Nxf6+ Qxf6 Rfd1 Re8 Qxd6 Bg4 Qxf6 gxf6 Rd3 Bxf3 Rxf3 Rd8 Rxf6 Kg7 Rf3 Rd2 Rg3+ Kf8 c3 Re2 f3 Rc2 Rg5 f6 Rh5 Kg7 Rd1 Kg6 Rh3 Rxc3 Rd7 Rc1+ Kf2 Rc2+ Kg3 h5 Rxb7 Kg5 Rxa7 h4+ Rxh4 Rxg2+ Kxg2 Kxh4 b6 Kg5 b7 f5 exf5 Kxf5 b8=Q e4 Rf7+ Kg5 Qg8+ Kh6 Rh7#C415Philidor DefensePhilidor DefenseUnknownUnknown54
56False5DrawDraw10+0trelynn171250franklin145321002e4 c5 Nf3 Qa5 a3B274Sicilian Defense: Mongoose VariationSicilian DefenseUnknownMongoose Variation248
67True33ResignWhite10+0capa_jr1520daniel_likes_chess1423d4 d5 e4 dxe4 Nc3 Nf6 f3 exf3 Nxf3 Nc6 Bb5 a6 Bd3 Bg4 O-O Nxd4 Be2 Nxe2+ Qxe2 Bxf3 Qxf3 Qd4+ Be3 Qg4 Qxb7 Rd8 Qc6+ Nd7 Nd5 e6 Nxc7+ Ke7 Bd2D0010Blackmar-Diemer Gambit: Pietrowsky DefenseBlackmar-Diemer GambitUnknownPietrowsky Defense97
78False9ResignBlack15+30daniel_likes_chess1413soultego2108e4 Nc6 d4 e5 d5 Nce7 c3 Ng6 b4B005Nimzowitsch Defense: Kennedy Variation | Linksspringer VariationNimzowitsch DefenseUnknownKennedy Variation-695
89True66ResignBlack15+0ehabfanri1439daniel_likes_chess1392e4 e5 Bc4 Nc6 Nf3 Nd4 d3 Nxf3+ Qxf3 Nf6 h3 Bc5 a3 O-O Be3 Bxe3 Qxe3 Re8 Qf3 c6 Nc3 b5 Bb3 Qa5 O-O Bb7 Ne2 c5 Rfd1 d6 c4 bxc4 dxc4 Rac8 Rd5 Bxd5 exd5 Qb6 Nc1 e4 Qf4 Nh5 Qg4 Nf6 Qf4 Qa5 Bc2 Qe1+ Kh2 Nh5 Qxd6 Qxf2 Bxe4 Rxe4 Nd3 Qg3+ Kg1 Qxd3 Rf1 Rce8 Qd7 Qe3+ Kh1 Ng3+ Kh2 Nxf1+C506Italian Game: Schilling-Kostic GambitItalian GameUnknownSchilling-Kostic Gambit47
910True119MateWhite10+0daniel_likes_chess1381mirco251209e4 d5 exd5 Qxd5 Nc3 Qe5+ Be2 Na6 d4 Qf5 Bxa6 bxa6 Nf3 Qe6+ Be3 Bb7 Ng5 Qc4 Qh5 Bxg2 Qxf7+ Qxf7 Nxf7 Kxf7 Rg1 Bf3 Rg3 Bh5 Rh3 g6 Ne4 Nf6 Nc5 e6 Nxa6 Rc8 Bf4 Bg4 Rg3 c6 Nb4 Bxb4+ c3 Ba5 b4 Bc7 Bxc7 Rxc7 Rb1 e5 dxe5 Re8 f4 Rd7 Kf2 Rd2+ Kg1 Rxa2 exf6 Kxf6 Rxg4 Kf5 Rg2 Rxg2+ Kxg2 Re2+ Kf3 Re4 Ra1 Rxf4+ Kg3 Rg4+ Kh3 h5 Rxa7 g5 Rf7+ Kg6 Rf3 Rf4 Rxf4 gxf4 Kg2 Kg5 c4 h4 b5 cxb5 cxb5 Kg4 b6 f3+ Kf2 Kf4 b7 h3 b8=Q+ Ke4 Qb4+ Kd5 Kxf3 Ke5 Kg3 Kf6 Qe4 Kg5 Qf3 Kh6 Qg4 Kh7 Qg5 Kh8 Kf4 Kh7 Kf5 Kh8 Kf6 Kh7 Qg7#B014Scandinavian Defense: Mieses-Kotroc VariationScandinavian DefenseUnknownMieses-Kotroc Variation172
game_idratedturnsvictory_statuswinnertime_incrementwhite_idwhite_ratingblack_idblack_ratingmovesopening_codeopening_movesopening_fullnameopening_shortnameopening_responseopening_variationrating_difference
2004820049True25ResignWhite10+10mateuslichess1252jamboger1233e4 e6 d4 d5 exd5 Qxd5 Nc3 Bb4 a3 Bxc3+ bxc3 Ne7 Qf3 Qd7 Ne2 Nbc6 Nf4 e5 dxe5 Nxe5 Qe4 f5 Qxe5 Qc6 Bb5C015French Defense: Exchange VariationFrench DefenseUnknownExchange Variation19
2004920050True43MateWhite10+0jkubb291328jamboger1252e4 e6 Nf3 d5 Nc3 Bb4 exd5 exd5 d4 Bg4 a3 Bxc3+ bxc3 Nf6 Be2 Ne4 Ne5 Nxc3 Qd3 Nxe2 Nxg4 Nxc1 Rxc1 O-O O-O Qh4 g3 Qxg4 Rce1 Nc6 Re2 Rae8 Rxe8 Rxe8 f3 Qh3 g4 Re6 Qd2 Rf6 Re1 Rxf3 Re8#C005French Defense: Two Knights VariationFrench DefenseUnknownTwo Knights Variation76
2005020051True9Out of TimeWhite10+0jamboger1243yamaguchipolgar1142c4 e5 d4 exd4 Qxd4 Nf6 Bg5 Be7 e4A202English Opening: King's English VariationEnglish OpeningUnknownKing's English Variation101
2005120052True58MateBlack10+10samael881237jamboger1231e4 e6 Nf3 d5 Bb5+ Bd7 c4 c6 Ba4 Qa5 b3 b5 cxb5 cxb5 b4 Bxb4 Bb3 dxe4 Nd4 Nc6 Bb2 e5 Nc2 Nf6 Nc3 Bg4 Ne2 e3 Nxe3 Ne4 f3 Bxd2+ Kf1 Bxe3 fxg4 Nd2+ Ke1 Nxb3+ Nc3 Nxa1 Bxa1 Nd4 Kf1 Rd8 Qd3 Bc1 Qe4 Qa3 Qxe5+ Ne6 Nxb5 Qd3+ Kf2 Bf4 Qb2 Qe3+ Kf1 Rd1#C003French Defense: Knight VariationFrench DefenseUnknownKnight Variation6
2005220053True37ResignWhite10+10jamboger1219samael881250c4 e6 d4 b6 Nc3 Bb7 Nf3 g6 h4 Bg7 Bg5 f6 Bf4 d6 e4 Ne7 d5 e5 Be3 c6 b4 c5 a3 h6 Qa4+ Nd7 Rb1 Qc7 bxc5 bxc5 Nb5 Qb8 Qa5 a6 Nc7+ Qxc7 Qxc7A404English DefenseEnglish DefenseUnknownUnknown-31
2005320054True24ResignWhite10+10belcolt1691jamboger1220d4 f5 e3 e6 Nf3 Nf6 Nc3 b6 Be2 Bb7 O-O Be7 Ne5 d6 Bh5+ g6 Nxg6 hxg6 Bxg6+ Kf8 e4 fxe4 Re1 d5A802Dutch DefenseDutch DefenseUnknownUnknown471
2005420055True82MateBlack10+0jamboger1233farrukhasomiddinov1196d4 d6 Bf4 e5 Bg3 Nf6 e3 exd4 exd4 d5 c3 Bd6 Bd3 O-O Nd2 Re8+ Kf1 Bxg3 hxg3 b6 g4 Ba6 g5 Bxd3+ Ne2 Ne4 Nxe4 Bxe4 Nf4 Qxg5 Nh3 Bxg2+ Kg1 Qg6 Nf4 Qg5 Nxg2 Re6 Qd3 Rh6 Qe2 Nc6 Re1 g6 f4 Rxh1+ Kxh1 Qh6+ Kg1 a5 Qb5 Na7 Re8+ Rxe8 Qxe8+ Kg7 Qe5+ Kg8 Qxc7 Qh5 Qxa7 Qd1+ Kh2 Qa1 Nh4 Qxb2+ Kh3 Qxc3+ Kg4 Qxd4 Qb8+ Kg7 Nf3 Qa1 Kg5 Qxa2 Ne5 Qg2+ Kh4 h5 Nxf7 Qg4#A412Queen's PawnQueen's PawnUnknownUnknown37
2005520056True35MateWhite10+0jamboger1219schaaksmurf31286d4 d5 Bf4 Nc6 e3 Nf6 c3 e6 Nf3 Be7 Bd3 O-O Nbd2 b6 Ne5 Nxe5 Bxe5 Nd7 Bxh7+ Kxh7 Qh5+ Kg8 Nf3 f6 Bf4 g5 Qg6+ Kh8 Nh4 Qe8 Qh6+ Kg8 Ng6 Kf7 Qh7#D003Queen's Pawn Game: Mason AttackQueen's Pawn GameUnknownMason Attack-67
2005620057True109ResignWhite10+0marcodisogno1360jamboger1227e4 d6 d4 Nf6 e5 dxe5 dxe5 Qxd1+ Kxd1 Nd5 c4 Nb6 c5 Nd5 Bc4 e6 Bxd5 exd5 Nc3 d4 Ne4 Bf5 f3 Nd7 b4 Nxe5 Bf4 f6 g4 Bxe4 fxe4 c6 Bxe5 fxe5 Nf3 Be7 Nxe5 Bf6 Nc4 O-O-O h4 h6 e5 b5 cxb6 Be7 bxa7 Kc7 a3 Rhf8 Kd2 Rf4 Rag1 d3 h5 Rf2+ Ke3 Re2+ Kf4 Rf8+ Kg3 Re3+ Nxe3 d2 Rd1 Bg5 Nf5 Kb7 Rhf1 Kxa7 Nd6 Rxf1 Rxf1 Kb6 e6 Kc7 Nf5 Kc8 e7 Kd7 a4 Bxe7 Nxe7 Kxe7 a5 Kd7 Rd1 Kc7 Rxd2 Kb7 Ra2 Ka6 Kf4 Kb5 a6 Kb6 a7 Kb5 a8=Q Kxb4 Qxc6 g5+ hxg6 Kb3 Rc2 Kb4 Qb7+ Ka3 Rc8B074Pirc DefensePirc DefenseUnknownUnknown133
2005720058True78MateBlack10+0jamboger1235ffbob1339d4 d5 Bf4 Na6 e3 e6 c3 Nf6 Nf3 Bd7 Nbd2 b5 Bd3 Qc8 e4 b4 e5 Ne4 Nxe4 dxe4 Bxe4 bxc3 Bxa8 Qxa8 bxc3 Ba3 Rb1 c5 Qd3 O-O Qxa6 Bc6 Qxa3 Bxf3 gxf3 Qxf3 Qxa7 Qxh1+ Ke2 Qxb1 Qxc5 Qc2+ Ke3 Qxa2 Qb4 h6 c4 g5 Bg3 Qa8 c5 Rb8 Qc3 f5 f4 Qe4+ Kd2 Qg2+ Kd3 gxf4 Bxf4 Qf3+ Kc4 Qxf4 c6 Qf1+ Kc5 Rb1 Qg3+ Kf7 c7 Rc1+ Kd6 Qa6+ Kd7 Qb5+ Kd8 Qe8#D003Queen's Pawn Game: Mason AttackQueen's Pawn GameUnknownMason Attack-104